Road Network Topology-aware Trajectory Representation Learning

Abstract

The approaches developed for task trajectory representation learning(TRL) on road networks can be divided into the following two categories,i.e.,recurrent neural network(RNN) and long short-term memory (LSTM) based sequence models,and the self-attention mechanism based learning models.Despite the significant contributions of these studies,they still suffer from the following problems.(1)The methods designed for road network representation learning in existing work ignore the transition probability between connected road segments and cannot fully capture the topological structure of the given road network.(2)The self-attention mechanism based learning models perform better than sequence models on short and medium trajectories but underperform on long trajectories,as they fail to character the long-term semantic features of trajectories well.Motivated by these findings,this paper proposes a new trajectory representation learning model,namely trajectory representation learning on road networks via masked sequence to sequence network(TRMS).Specifically,the model extends the traditional algorithm DeepWalk with a probability-aware walk to fully capture the topological structure of road networks,and then utilizes the Masked Seq2Seq learning framework and self-attention mechanism in a unified manner to capture the long-term semantic features of tra-jectories.Finally,experiments on the real-world datasets demonstrate that TRMS outperforms the state-of-the-art methods in embedding short,medium,and long trajectories.